Models can be trained end-to-end via imitating an optimal solver at each step (i.e. supervised learning).
For models with NAR decoders, the edge predictions are linked to the ground-truth TSP tour by minimizing the binary cross-entropy loss for each edge [1]}, [2]}.
For AR architectures, at each step, we minimize the cross-entropy loss between the predicted probability distribution over all edges leaving the current node and the next node from the groundtruth tour, following Vinyals et al. [3]}.
We use teacher-forcing to stabilize training [4]}.
